Schedule Sessions' Table

Wednesday Oct 9

09:00 Registration & Coffee
10:30 Opening Session
10:50 Algo.Rules - How do we get the ethics into the code?
Carla Hustedt
11:20 Coffee Break Keynote Q&A Session
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  Python Performance Time Series Deployment Visualization Tutorial @ 11:30
11:50 Refactoring in Python: Design Patterns and Approaches
Tin Marković 		Boosting simulation performance with Python
Eran Friedman 		Time Series Anomaly Detection for Bottling Machine Maintenance
Andrea Spichtinger 		🌈Apache Airflow for beginners
Varya 		Visualizing Interactive Graph Networks in Python
Jan-Benedikt Jagusch 		Get to grips with pandas and scikit-learn
Sandrine Pataut 		Package and Dependency Management with Poetry
Steph Samson
12:25 Mock Hell
Edwin Jung 		Hide Code, Minimize Dependencies, Boost Performance - The PyTorch JIT
Tilman Krokotsch 		Time series modelling with probabilistic programming
Sean Matthews, Jannes Quer 		Practical DevOps for the busy data scientist
Dr. Tania Allard 		How to choose better colors for your data visualizations
Daniel Ringler
13:00 Lunch PyLadies Lunch @12:45
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  Micropython Prediction Julia NLP Feature Engineering Tutorial @ 13:50 -
14:00 TBC
James Powell 		Fighting fraud: finding duplicates at scale
Alexey Grigorev 		CANCELLED: First steps in Julia
Felicia Burtscher 		Where Linguistics meets Natural Language Processing
Mariana Capinel 		Automated Feature Engineering and Selection in Python
Franziska Horn 		Write your Own Decorators
Mike Müller 		UPDATED: Get to grips with pandas and scikit-learn, Part two ("Deep Learning for Healthcare with PyTorch" had to be canceled) "
14:35 How MicroPython went into space
Christine Spindler 		AI Intentions and Code Completion
Vasily Korf 		Julia for Python
Simon Danisch 		Why you should (not) train your own BERT model for different languages or domains
Marianne Stecklina 		Automating feature engineering for supervised learning? Methods, open-source tools and prospects.
Thorben Jensen
15:05 Coffee Break
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  Deployment ML use-case Text Storage/Micropython Enterprise Tutorial @ 15:20 -
15:30 Applying deployment oriented mindset for building Machine Learning models
Marianna Diachuk 		Python-Powered OSINT! Modernising Open Source Intelligence for Investigating Disinformation
Chiin-Rui Tan, Dare Imam-Lawal 		vtext: text processing in Rust with Python bindings
Roman Yurchak 		Kartothek – Table management for cloud object stores powered by Apache Arrow and Dask
Florian Jetter 		Developers vs. Enterprise
Ingo Stegmaier 		Hidden Markov Models for Chord Recognition - Intuition and Applications
Caio Miyashiro 		pytest - simple, rapid and fun testing with Python
Florian Bruhin
16:05 Production-level data pipelines that make everyone happy using Kedro
Yetunde Dada 		Creating an Interactive ML Conference Showcase
Harald Bosch 		Privacy-preserving Machine Learning for text processing
Sarah Diot-Girard 		Using Micropython to develop an IoT multimode sensor platform with an Augmented Reality UI
Nicholas Herriot 		Running An Open Source Project Like A Start Up
Cheuk Ting Ho
16:50 Community Space
17:00 PEP 581 and PEP 588: Migrating CPython's Issue Tracker
Mariatta Wijaya
17:45 Lightning Talks
18:30 END

Thursday Oct 10

08:30 Doors Open
09:00 Morning Announcements
09:10 Python 2020+
Łukasz Langa
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  Gaussian Process Docker Python's friends ML & uncertainty Tools Tutorial -
10:00 Gaussian Progress
Vincent Warmerdam 		Docker and Python - A Match made in Heaven
Dr. Hendrik Niemeyer 		Beyond Paradigms: a new key to grok Python & other languages
Luciano Ramalho 		Are you sure about that?! Uncertainty Quantification in AI
Florian Wilhelm 		Tools that help you get your experiments under control
Katharina Rasch 		Airflow: your ally for automating machine learning and data pipelines
Enrica Pasqua, Bahadir Uyarer 		Fairness in decision-making with AI: a practical guide & hands-on tutorial using Aequitas
Pedro Saleiro
10:50 Gaussian Process for Time Series Analysis
Dr. Juan Orduz 		6 Years of Docker: The Good, the Bad and Python Packaging
Sebastian Neubauer 		10 ways to debug Python code
Christoph Deil 		Embrace uncertainty! Why to go beyond point estimators for valuable ML applications
Stefan Maier 		A Tour of JupyterLab Extensions
Jeremy Tuloup
11:20 Coffee Break Keynote Q&A Session
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  Vision Enterprise API Visualization ML for good Tutorial @ 11:30 -
11:50 Equivariance in CNNs: how generalising the weight-sharing property increases data-efficiency
Marysia Winkels 		Data Literacy for Managers
Alexander CS Hendorf 		Break your API gently - or not at all
Tim Hoffmann 		Dash: Interactive Data Visualization Web Apps with no Javascript
Dom Weldon 		Want to have a positive social impact as a data scientist?
Ellen König 		Build a Machine Learning pipeline with Jupyter and Azure
Daniel Heinze 		An Introduction to Concurrency and Parallelism using Python Programming Language
Tanmoy Bandyopadhyay
12:25 Using adversarial samples to break and robustify your Vision Neural Network Models
Irina Vidal Migallón 		Avoiding ML FOBO
Rachel Berryman, Dânia Meira 		What if I tell you that your specs are broken
Samuele Maci 		Panel: Turn any notebook into a deployable dashboard
Philipp Rudiger 		Tackle the problems that really matter - leverage the power of data science in the service of humanity
Eva Schreyer, Lisa Zäuner
13:00 Lunch
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  Python scikit* Vision Tests and *env Classificaion / FP Tutorial @ 13:45 Open Space -
14:00 Python Panel
Alexander CS Hendorf, Hynek Schlawack, Mariatta Wijaya, Łukasz Langa, Stefan Behel 		skorch: A scikit-learn compatible neural network library that wraps pytorch
Benjamin Bossan 		Birds of a feather flock together - Tracking pigeons with Python and OpenCV
Neslihan Edes 		How to write tests that need a lot of data?
Sander Kooijmans 		10 Years of Automated Category Classification for Product Data
Johannes Knopp 		Kubernetes 101 for Python Developers
Christian Barra 		Sieer: Lessons Learned as Data Science Provider
14:35 Python Panel Current affairs, updates, and the roadmap of scikit-learn and scikit-learn-contrib
Adrin Jalali 		Detecting and Analyzing Solar Panels in Switzerland using Aerial Imagery
Martin Christen 		venv, pyenv, pypi, pip, pipenv, pyWTF?
Simone Robutti 		Dr. Schmood's Notebook of Python Calisthenics and Orthodontia
David Schmudde
15:05 Coffee Break
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  Python A bit of Theory Good practices ML use-case Hardware Tutorial @ 15:15 Open Space/PSV -
15:30 Static Typing in Python
Dustin Ingram 		Should I stay or should I go? Optimal exercise decisions using the Longstaff-Schwartz algorithm
Benedikt Rudolph 		From body and code <programming in times of acceptance>
Paloma 		Using Overhead Video Capture to Analyse Grouping Behaviour of Dancers in a Silent Disco
Nelson Mooren 		Getting started with FPGA with Python
Olga 		Quantum computing with Python
James Wootton 		Beyond 9 to 5
16:05 Is it me, or the GIL?
Christoph Heer 		Active Learning with Bayesian Nonnegative Matrix Factorization for Recommender Systems
Gönül Aycı 		Commenting code — beyond common wisdom
Stefan Schwarzer 		How strong is my opponent? Using Bayesian methods for skill assessment
Darina Goldin 		Chips Made From Python
Dan Fritchman 		PSV Mitgliederversammung @16:00
16:50 Community Space
17:00 Extended Ligthning Talks CANCELLED: Crunching Numbers Like a Journalist
Marie-Louise Timcke
17:45 Lightning Talks Keynote Q&A Session
18:30 END
19:00 IBM Party at PyConDE & PyData Berlin 2019

Friday Oct 11

08:30 Doors Open
09:00 Morning Announcements
09:10 Rethinking Open Source in the Era of Cloud & Machine Learning
Peter Wang
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  ML & Ethics Testing DB Production ML & RL Tutorial
10:00 Law, ethics and machine learning – a curious ménage à trois
Dr. Benjamin Werthmann 		Introduction to automated testing with pytest
Raphael Pierzina 		CANCELLED: Fresh New Pythonic Database: EdgeDB (And Why It's the Future)
Dmitry Nazarov 		Version Control for Data Science
Alessia Marcolini 		Why you don’t see many real-world applications of Reinforcement Learning.
Yurii Tolochko 		CANCELED: Create CUDA kernels from Python using Numba and CuPy.
Valentin Haenel 		Managing the end-to-end machine learning lifecycle with MLFlow
Tobias Sterbak
10:50 Transforming a Legacy System into a Bias-Mitigating AI Solution for Debt Repayment
Avaré Stewart 		Abridged metaprogramming classics - this episode: pytest
Oliver Bestwalter 		Strawberry: a dataclasses inspired approach to GraphQL
Patrick Arminio 		Monitoring infrastructure and application using Django, Sensu and Celery.
Hari Kishore Sirivella 		Loss Function Theory 101
David Wölfle
11:20 Coffee Break Keynote Q&A Session
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  Python Input Lessons Learned PyMC Carreer Tutorial @ 11:30 -
11:50 What’s new in Python 3.8?
Stéphane Wirtel 		Optimizing Input: Building your own customized keyboard
Daniel Rios 		What we learned from scraping 1 billion webpages every month
Samet Atdag 		Leveraging the advantages of Bayesian Methods to build a data science product using PyMC3
Korbinian Kuusisto 		Professional Development and Career Progression for Data Scientists
Noa Tamir 		Decentralized and Privacy-Preserving ML via TensorFlow Federated
Peter Kairouz, Amlan Chakraborty 		Parallel programming for python developers – Let’s Go(lang)
Dominik Henter, Jéssica Lins
12:25 A Medieval DSL? Parsing Heraldic Blazons with Python
Lady Red 		Your Name Is Invalid!
Miroslav Šedivý 		Driving 3D Printers with Python: Lessons Learned
Gina Häußge 		A Bayesian Workflow with PyMC and ArviZ
Corrie Bartelheimer 		Lessons Learned as a Product Manager in Data Science
Tereza Iofciu
13:00 Lunch Lunch
time Hauptsaal Saal 2 Saal 10 Saal 6 Saal 5 Saal 4 Saal 7 Lounge
  Work Interpretable ML NLP for good Visualization ML use-case Tutorial @ 13:45 Open Space -
14:00 Job Panel
Christian Barra, Tereza Iofciu, Katharina Rasch, Matteo Guzzo, Sieer Angar 		Interpretable Machine Learning: How to make black box models explainable
Alexander Engelhardt 		Does hate sound the same in all languages?
Andrada Pumnea 		Friend or Foe: Comparison of R & Python in Data Wrangling & Visualisation
Yuta Kanzawa 		Machine learning with little data - from digital twin to predictive maintenance
Andreas Hantsch 		Using machine learning for Level Generation in Snake (video-game)
Filipe Silva 		Open Space # 4
14:35 Job panel: Freelancing / academia to industry Event-Sourced Story
Jacek Kołodziej 		The Sound of Silence: Online Misogyny and How we Model it
Teresa Ingram 		Making the complex simple in data viz
Tania Vasilikioti 		Take control of your hearing: Accessible methods to build a smart noise filter
Peggy Sylopp, Aislyn Rose 		Open Space # 5
15:05 Coffee Break
15:30 Sprint Orientation
15:45 Lightning Talks
16:15 Closing Session
16:45 END
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